LASPATED: A Library for the Analysis of Spatio-Temporal Discrete Data (User Manual)

TL;DR

LASPATED is a comprehensive Python library with supplementary Matlab and C++ packages designed for analyzing and calibrating probabilistic models of stochastic spatio-temporal data, facilitating research and application in this domain.

Contribution

This paper introduces LASPATED, a new software library that provides tools for analyzing and calibrating models of stochastic spatio-temporal data, with implementations across multiple programming languages.

Findings

Library available on GitHub with extensive tools.

Includes calibration methods for probabilistic models.

Provides tutorials and documentation for users.

Abstract

This is the User Manual of the LASPATED library. This library is available on GitHub (at https://github.com/vguigues/LASPATED)) and provides a set of tools to analyze spatiotemporal data. A video tutorial for this library is available on Youtube. It is made of a Python package for time and space discretizations and of two packages (one in Matlab and one in C++) implementing the calibration of the probabilistic models for stochastic spatio-temporal data proposed in the companion paper arXiv:2203.16371v2.